1. Field of the Invention
This invention relates in general to database management systems performed by computers, and in particular, to improving multi-dimensional restructure performance when adding or removing dimensions and dimension members.
2. Description of Related Art
Relational DataBase Management System (RDBMS) software using a Structured Query Language (SQL) interface is well known in the art. The SQL interface has evolved into a standard language for RDBMS software and has been adopted as such by both the American National Standards Institute (ANSI) and the International Standards Organization (ISO).
RDBMS software has typically been used with databases comprised of traditional data types that are easily structured into tables. However, RDBMS products do have limitations with respect to providing users with specific views of data. Thus, xe2x80x9cfront-endsxe2x80x9d have been developed for RDBMS products so that data retrieved from the RDBMS can be aggregated, summarized, consolidated, summed, viewed, and analyzed. However, even these xe2x80x9cfront-endsxe2x80x9d do not easily provide the ability to consolidate, view, and analyze data in the manner of xe2x80x9cmulti-dimensional data analysis.xe2x80x9d This type of functionality is also known as on-line analytical processing (OLAP).
OLAP generally comprises numerous, speculative xe2x80x9cwhat-ifxe2x80x9d and/or xe2x80x9cwhyxe2x80x9d data model scenarios executed by a computer. Within these scenarios, the values of key variables or parameters are changed, often repeatedly, to reflect potential variances in measured data. Additional data is then synthesized through animation of the data model. This often includes the consolidation of projected and actual data according to more than one consolidation path or dimension.
Data consolidation is the process of synthesizing data into essential knowledge. The highest level in a data consolidation path is referred to as that data""s dimension. A given data dimension represents a specific perspective of the data included in its associated consolidation path. There are typically a number of different dimensions from which a given pool of data can be analyzed. This plural perspective, or Multi-Dimensional Conceptual View, appears to be the way most business persons naturally view their enterprise. Each of these perspectives is considered to be a complementary data dimension. Simultaneous analysis of multiple data dimensions is referred to as multi-dimensional data analysis.
OLAP functionality is characterized by dynamic multi-dimensional analysis of consolidated data supporting end user analytical and navigational activities including:
calculations and modeling applied across dimensions, through hierarchies and/or across members;
trend analysis over sequential time periods;
slicing subsets for on-screen viewing;
drill-down to deeper levels of consolidation;
reach-through to underlying detail data; and
rotation to new dimensional comparisons in the viewing area.
OLAP is often implemented in a multi-user client/server mode and attempts to offer consistently rapid response to database access, regardless of database size and complexity. While some vendors have proposed and offered OLAP systems that use RDBMS products as storage managers, to date these offerings have been unsuccessful for a variety of reasons.
A multi-dimensional OLAP system has multiple dimensions and members within the dimensions. The data for these dimensions and members may be stored in a table. Then, if these dimensions are changed,.data in the table is modified. In particular, a large numbers of rows may be deleted (i.e., removed) from a relational database table in a single unit of work. Deleting a large number of rows may lead to several problems. First, performance may be slow because of the volume of data being deleted and because the database manager logs all these changes. Second, the database manager may run out of log space. That is, some database managers have an upper limit to the size of the transaction log file. If enough rows are deleted this maximum log file size will be reached.
Thus, there is a need in the art for improving multi-dimensional restructure performance when adding or removing dimensions and dimension members.
To overcome the limitations in the prior art described above, and to overcome other limitations that will become apparent upon reading and understanding the present specification, the present invention discloses a method, apparatus, and article of manufacture for improving multi-dimensional restructure performance when adding or removing dimensions and dimension members.
According to an embodiment of the invention, a command is executed in a computer to perform a database operation on a relational database stored on a data store connected to the computer. It is determined that a multi-dimensional database has been modified. Then, it is determined that the modified multi-dimensional database requires modifications to one or more original tables in a relational database corresponding to the multi-dimensional database. The modifications are incorporated into one or more new tables by copying data from the original tables into the new tables.